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This review would summarize recent advancements in predictive analytics within the financial industry, exploring both the technological developments and their philosophical implications. It would evaluate the balance between data-driven decision-making and ethical considerations. Predictive analytics has witnessed remarkable advancements, blending philosophical considerations with practical applications to redefine decision-making processes across various industries. This abstract provides an overview of the philosophical underpinnings and practical implications of the evolving landscape of predictive analytics. Philosophically, predictive analytics raises profound questions about determinism, human agency, and the ethical implications of data-driven decision-making. The abstract explores the tension between the predictive power of algorithms and the need to preserve individual autonomy, delving into the ethical considerations surrounding privacy, bias, and accountability. Practically, the overview navigates the cutting-edge tools and techniques that drive predictive analytics. From machine learning algorithms to big data analytics, the abstract examines how these technologies empower organizations to make data-driven predictions, optimize processes, and gain actionable insights. Real-world applications in business, healthcare, finance, and other domains underscore the transformative impact of predictive analytics on operational efficiency and strategic decision-making. As predictive analytics continues to shape the future of information processing, this abstract encapsulates the dual nature of its evolution – a philosophical exploration of its ethical dimensions and a practical examination of its applications, offering a holistic understanding of the field's implications for individuals, organizations, and society at large.
Farayola et al. (Mon,) studied this question.